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Relevant bibliographies by topics / Neural network controllers

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Academic literature on the topic 'Neural network controllers'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Neural network controllers"

1

Yamada, Takayuki, and Tetsuro Yabuta. "Adaptive Neural Network Controllers for Dynamics Systems." Journal of Robotics and Mechatronics 2, no. 4 (1990): 245–57. http://dx.doi.org/10.20965/jrm.1990.p0245.

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Many studies such as Kawato's work have been undertaken in order to apply both the flexibility and learning ability of neural networks to dynamic system controllers. However, their characteristics have not yet been completely clarified. On the other hand, many studies have established conventional control theories such as adaptive control. If we can clarify the relationship between neural network controllers and adaptive controllers, the two control algorithms will be developed considerably by making use of the advantages of each. Therefore, this paper proposes a neural network direct controll

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Günther, Johannes, Elias Reichensdörfer, Patrick M. Pilarski, and Klaus Diepold. "Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison." PLOS ONE 15, no. 12 (2020): e0243320. http://dx.doi.org/10.1371/journal.pone.0243320.

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Modern automation systems largely rely on closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. These systems are increasingly complex, yet most deployed controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but their simplicity is at odds with the robustness required to reliably control complex processes. Modern machine learning techniques offer a way to extend PID controllers beyond their linear control capabilities by using neural networks. However,

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Scott, Gary M., Jude W. Shavlik, and W. Harmon Ray. "Refining PID Controllers Using Neural Networks." Neural Computation 4, no. 5 (1992): 746–57. http://dx.doi.org/10.1162/neco.1992.4.5.746.

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The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the MANNCON (Multivariable Artificial Neural Network Control) algorithm by which the mathematical equations governing a PID (Proportional-Integral-Derivative) controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the outflow and temperature of a water tank, producing statisticall

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Lippe, Wolfram-M., Steffen Niendieck, and Andreas Tenhagen. "On the Optimization of Fuzzy-Controllers by Neural Networks." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 158–63. http://dx.doi.org/10.20965/jaciii.1999.p0158.

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Methods are known for combining fuzzy-controllers with neural networks. One of the reasons of these combinations is to work around the fuzzy controllers’ disadvantage of not being adaptive. It is helpful to represent a given fuzzy controller by a neural network and to have rules adapted by a special learning algorithm. Some of these methods are applied in the NEFCONmode or the model of Lin and Lee. Unfortunately, none adapts all fuzzy-controller components. We suggest a new model enabling the user to represent a given fuzzy controller by a neural network and adapt its components as desired.

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Darsivan, Fadly Jashi, Wahyudi Martono, and Waleed F. Faris. "Active Engine Mounting Control Algorithm Using Neural Network." Shock and Vibration 16, no. 4 (2009): 417–37. http://dx.doi.org/10.1155/2009/257480.

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This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the c

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Woodford, Grant W., and Mathys C. du Plessis. "Complex Morphology Neural Network Simulation in Evolutionary Robotics." Robotica 38, no. 5 (2019): 886–902. http://dx.doi.org/10.1017/s0263574719001140.

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SUMMARYThis paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dim

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Ivanov, Radoslav, Kishor Jothimurugan, Steve Hsu, Shaan Vaidya, Rajeev Alur, and Osbert Bastani. "Compositional Learning and Verification of Neural Network Controllers." ACM Transactions on Embedded Computing Systems 20, no. 5s (2021): 1–26. http://dx.doi.org/10.1145/3477023.

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Recent advances in deep learning have enabled data-driven controller design for autonomous systems. However, verifying safety of such controllers, which are often hard-to-analyze neural networks, remains a challenge. Inspired by compositional strategies for program verification, we propose a framework for compositional learning and verification of neural network controllers. Our approach is to decompose the task (e.g., car navigation) into a sequence of subtasks (e.g., segments of the track), each corresponding to a different mode of the system (e.g., go straight or turn). Then, we learn a sep

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Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

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For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis

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Wilamowski, B. M., J. Binfet, and M. O. Kaynak. "VLSI Implementation of Neural Networks." International Journal of Neural Systems 10, no. 03 (2000): 191–97. http://dx.doi.org/10.1142/s012906570000017x.

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Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has

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Clitan, Iulia, Mihai Abrudean, and Vlad Mureşan. "Design of Neural Network Controllers for the Horizontal Positioning of an Industrial Manipulator." Applied Mechanics and Materials 555 (June 2014): 281–87. http://dx.doi.org/10.4028/www.scientific.net/amm.555.281.

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This paper presents the design of neural network controllers for the electro-hydraulically driven positioning system of an industrial manipulator. The manipulator is represented by an unloading machine that extracts the billets from a rotary hearth furnace. The design of a Narma-L2 controller and a Model-reference controller is presented. Neural network controllers can be used for the modeling and control of dynamical systems as long as a suitable neural network is chosen. The obtained controllers are compared on the basis of overall performances. The assessment of the results is done by means

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Dissertations / Theses on the topic "Neural network controllers"

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Balaam, Andy. "Exploring developmental dynamics in evolved neural network controllers." Thesis, University of Sussex, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426199.

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Kimball, Nicholas. "Utilizing Trajectory Optimization In The Training Of Neural Network Controllers." DigitalCommons@CalPoly, 2019. https://digitalcommons.calpoly.edu/theses/2071.

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Applying reinforcement learning to control systems enables the use of machine learning to develop elegant and efficient control laws. Coupled with the representational power of neural networks, reinforcement learning algorithms can learn complex policies that can be difficult to emulate using traditional control system design approaches. In this thesis, three different model-free reinforcement learning algorithms, including Monte Carlo Control, REINFORCE with baseline, and Guided Policy Search are compared in simulated, continuous action-space environments. The results show that the Guided Pol

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Chan, Yat-fei. "Neurofuzzy network based adaptive nonlinear PID controllers." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43958357.

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Chan, Yat-fei, and 陳一飛. "Neurofuzzy network based adaptive nonlinear PID controllers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43958357.

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Hutt, Benjamin David. "Evolving artificial neural network controllers for robots using species-based methods." Thesis, University of Reading, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270831.

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Rathbone, Kevin. "Evolving visually guided neural network robot arm controllers for lifetime learning." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327646.

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Smith, Bradley R. "Neural Network Enhancement of Closed-Loop Controllers for Ill-Modeled Systems with Unknown Nonlinearities." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29607.

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The nonlinearities of a nonlinear system can degrade the performance of a closed-loop system. In order to improve the performance of the closed-loop system, an adaptive technique, using a neural network, was developed. A neural network is placed in series between the output of the fixed-gain controller and the input into the plant. The weights are initialized to values that result in a unity gain across the neural network, which is referred to as a "feed-through neural network." The initial unity gain causes the output of the neural network to be equal to the input of neural network at the

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Massera, Gianluca. "Evolution of grasping behaviour in anthropomorphic robotic arms with embodied neural controllers." Thesis, University of Plymouth, 2012. http://hdl.handle.net/10026.1/1172.

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The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced se

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Liu, Jingrong. "Design and Analysis of Intelligent Fuzzy Tension Controllers for Rolling Mills." Thesis, University of Waterloo, 2002. http://hdl.handle.net/10012/848.

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This thesis presents a fuzzy logic controller aimed at maintaining constant tension between two adjacent stands in tandem rolling mills. The fuzzy tension controller monitors tension variation by resorting to electric current comparison of different operation modes and sets the reference for speed controller of the upstream stand. Based on modeling the rolling stand as a single input single output linear discrete system, which works in the normal mode and is subject to internal and external noise, the element settings and parameter selections in the design of the fuzzy controller are discu

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Mohammadzadeh, Soroush. "System identification and control of smart structures: PANFIS modeling method and dissipativity analysis of LQR controllers." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/868.

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"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy in

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Books on the topic "Neural network controllers"

1

Blake, Joseph. Neural network controllers: Software implementation and a hardware implementation based on a reconfigurable computing application. The Author], 1996.

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Haynes, B. P. A neural network adaptive controller for non-linear systems. University of Portsmouth, Faculty of Technology, 1997.

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3

Jorgensen, Charles C. Distributed memory approaches for robotic neural controllers. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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Jorgensen, Charles C. Development of a sensor coordinated kinematic model for neural network controller training. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.

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5

Rylatt, R. Mark. Investigations into controllers for adaptive autonomous agents based on artificial neural networks. De Montfort University, 2001.

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6

Two neural network algorithms for designing optimal terminal controllers with open final-time. NASA Ames Research Center, 1992.

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7

Adaptive Neural Network Controller for ATM Traffic. Storming Media, 1996.

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8

Denise, Taylor Lynore, and United States. National Aeronautics and Space Administration., eds. Artificial neural network implementation of a near-ideal error prediction controller. Dept. of Electrical Engineering, School of Engineering and Applied Science, University of Virginia, 1992.

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Artificial neural network implementation of a near-ideal error prediction controller. Dept. of Electrical Engineering, School of Engineering and Applied Science, University of Virginia, 1992.

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10

Supervised Sequence Labelling With Recurrent Neural Networks. Springer, 2012.

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Book chapters on the topic "Neural network controllers"

1

Pham, Duc Truong, and Xing Liu. "Neural Network Controllers." In Neural Networks for Identification, Prediction and Control. Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3244-8_6.

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Zhao, Hengjun, Xia Zeng, Taolue Chen, Zhiming Liu, and Jim Woodcock. "Learning Safe Neural Network Controllers with Barrier Certificates." In Dependable Software Engineering. Theories, Tools, and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62822-2_11.

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Lichtensteiger, Lukas, and Rolf Pfeifer. "An Optimal Sensor Morphology Improves Adaptability of Neural Network Controllers." In Artificial Neural Networks — ICANN 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_138.

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Ferreira, Enrique D., and Bruce H. Krogh. "Switching controllers based on neural network estimates of stability regions and controller performance." In Hybrid Systems: Computation and Control. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64358-3_36.

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Gross, Clemens, and Hendrik Voelker. "A Comparison of Tuning Methods for PID-Controllers with Fuzzy and Neural Network Controllers." In Cyber-Physical Systems and Control. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34983-7_8.

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Pujol, João Carlos Figueira, and Riccardo Poli. "Dual network representation applied to the evolution of neural controllers." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0040815.

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Hussin, Mahmoud F., Badr M. Abouelnasr, and Amin A. Shoukry. "Comparative Study of Neural Network Controllers for Nonlinear Dynamic Systems." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45486-1_30.

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Yang, Zhengfeng, Yidan Zhang, Wang Lin, et al. "An Iterative Scheme of Safe Reinforcement Learning for Nonlinear Systems via Barrier Certificate Generation." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_22.

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AbstractIn this paper, we propose a safe reinforcement learning approach to synthesize deep neural network (DNN) controllers for nonlinear systems subject to safety constraints. The proposed approach employs an iterative scheme where a learner and a verifier interact to synthesize safe DNN controllers. The learner trains a DNN controller via deep reinforcement learning, and the verifier certifies the learned controller through computing a maximal safe initial region and its corresponding barrier certificate, based on polynomial abstraction and bilinear matrix inequalities solving. Compared with the existing verification-in-the-loop synthesis methods, our iterative framework is a sequential synthesis scheme of controllers and barrier certificates, which can learn safe controllers with adaptive barrier certificates rather than user-defined ones. We implement the tool SRLBC and evaluate its performance over a set of benchmark examples. The experimental results demonstrate that our approach efficiently synthesizes safe DNN controllers even for a nonlinear system with dimension up to 12.

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Ivanov, Radoslav, Taylor Carpenter, James Weimer, Rajeev Alur, George Pappas, and Insup Lee. "Verisig 2.0: Verification of Neural Network Controllers Using Taylor Model Preconditioning." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_11.

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AbstractThis paper presents Verisig 2.0, a verification tool for closed-loop systems with neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop a Taylor-model-based reachability algorithm through Taylor model preconditioning and shrink wrapping. Furthermore, we provide a parallelized implementation that allows Verisig 2.0 to efficiently handle larger NNs than existing tools can. We provide an extensive evaluation over 10 benchmarks and compare Verisig 2.0 against three state-of-the-art verification tools. We show that Verisig 2.0 is both more accurate and faster, achieving speed-ups of up to 21x and 268x against different tools, respectively.

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Zhao, Longyang, Xiao Zhu, Haoming Yang, and Xuanju Dang. "Design of Hybrid Controllers Based on Radial Basis Function Neural Network." In Lecture Notes in Electrical Engineering. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4796-1_26.

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Conference papers on the topic "Neural network controllers"

1

Scott, R. W., and D. J. Collins. "Neural network adaptive controllers." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137872.

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Kumar, Manish, and Devendra P. Garg. "Neural Network Based Intelligent Learning of Fuzzy Logic Controller Parameters." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59589.

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Design of an efficient fuzzy logic controller involves the optimization of parameters of fuzzy sets and proper choice of rule base. There are several techniques reported in recent literature that use neural network architecture and genetic algorithms to learn and optimize a fuzzy logic controller. This paper presents methodologies to learn and optimize fuzzy logic controller parameters that use learning capabilities of neural network. Concepts of model predictive control (MPC) have been used to obtain optimal signal to train the neural network via backpropagation. The strategies developed have

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Veloz, Alejandro, Juan C. Romero Quintini, Mónica Parada, and Sergio E. Diaz. "Experimental Testing of a Magnetically Levitated Rotor With a Neural Network Controller." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69120.

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Magnetic bearings represent a solution for high rotating speeds and sterile environments where lubrication fluids could contaminate. They can also be used in systems where maintenance is difficult or inaccessible, because they don’t require auxiliary lubrication systems and don’t suffer mechanic wear as they work with no contact between rotor and bearing stator. An important part of magnetic bearings is the controller; which is needed to stabilize the system. This controller is generally a PID in which tuning and/or filters design can be complicated for not well known systems. This work presen

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Hong Wang. "Towards stable neural network controllers." In International Conference on Control '94. IEE, 1994. http://dx.doi.org/10.1049/cp:19940184.

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Dieulot, J. Y., P. Borne, and W. Mrizak. "Composite Multimodel and Neural Network Controllers." In Multiconference on "Computational Engineering in Systems Applications. IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.4281705.

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Kis, Karol, Martin Klauco, and Alajos Meszaros. "Neural Network Controllers in Chemical Technologies." In 2020 IEEE 15th International Conference of System of Systems Engineering (SoSE). IEEE, 2020. http://dx.doi.org/10.1109/sose50414.2020.9130425.

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Batayneh, Wafa, and Nash’at Nawafleh. "Comparative Study of DC Motor Speed Control Using Neural Networks and Fuzzy Logic Controller." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51362.

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This paper demonstrates the importance of the intelligent controllers over the conventional methods. A speed control of the DC motor is developed using both Neural Networks and Fuzzy logic controller in MATLAB environment as intelligent controllers. In addition a conventional PID controller is developed for comparison purposes. Both intelligent controllers are designed based on the simulation results of the nonlinear equations in addition to the expert pre knowledge of the system. The output response of the system is obtained using the two types of the intelligent controllers, in addition to t

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Shukla, D., D. M. Dawson, and F. W. Paul. "Multiple neural network based DCAL controllers using orthonormal activation function neural networks." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.610811.

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Patre, Parag M., Shubhendu Bhasin, Zachary D. Wilcox, and Warren E. Dixon. "Composite adaptation for neural network-based controllers." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400453.

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Calise, Anthony, and mann Sharma. "Neural network augmentation of existing linear controllers." In AIAA Guidance, Navigation, and Control Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-4163.

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Reports on the topic "Neural network controllers"

1

Braganza, D., D. M. Dawson, I. D. Walker, and N. Nath. Neural Network Grasping Controller for Continuum Robots. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada462583.

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Patro, S., and W. J. Kolarik. Integrated evolutionary computation neural network quality controller for automated systems. Office of Scientific and Technical Information (OSTI), 1999. http://dx.doi.org/10.2172/350895.

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Vitela, J. E., U. R. Hanebutte, and J. Reifman. An artificial neural network controller for intelligent transportation systems applications. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/219376.

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Russell, Chris A., and Glenn F. Wilson. Application of Artificial Neural Networks for Air Traffic Controller Functional State Classification. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada404631.

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Blinov, D. O., A. I. Fomin, and A. A. Chibin. Neural network model for determining the values of the indicator of the effectiveness of the impact of controlled means on air objects. OFERNiO, 2021. http://dx.doi.org/10.12731/ofernio.2021.24804.

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Nikiforov, Vladimir. The use of composite materials in smart medical equipment, including with innovative laser systems, controlled and controlled complexes with elements of artificial intelligence and artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2133.

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Nikiforov, Vladimir. The use of composite materials in smart medical equipment, including usage of innovative laser systems, controlled and monitored by complexes with elements of artificial intelligence and artificial neural networks. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2171.

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